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20242026
most citedLarge language models can help boost food production, but be mindful of their risks

5 citations · 10 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.LG2024★ 1 cited

Can sparse autoencoders be used to decompose and interpret steering vectors?

Harry Mayne, Yushi Yang, Adam Mahdi

Steering vectors are a promising approach to control the behaviour of large language models. However, their underlying mechanisms remain poorly understood. While sparse autoencoder…

cs.LG2024★ 2 cited

How Does DPO Reduce Toxicity? A Mechanistic Neuron-Level Analysis

Yushi Yang, Filip Sondej, Harry Mayne +2

Safety fine-tuning algorithms reduce harmful outputs in language models, yet their mechanisms remain under-explored. Direct Preference Optimization (DPO) is a popular choice of alg…

cs.CL2024★ 1 cited

LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages

Andrew M. Bean, Simi Hellsten, Harry Mayne +5

In this paper, we present the LingOly benchmark, a novel benchmark for advanced reasoning abilities in large language models. Using challenging Linguistic Olympiad puzzles, we eval…

cs.CY2024★ 5 cited

Large language models can help boost food production, but be mindful of their risks

Djavan De Clercq, Elias Nehring, Harry Mayne +1

Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching ex…

cs.LG2024★ 1 cited

Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?

Harry Mayne, Guy Parsons, Adam Mahdi

The use of unsupervised learning to identify patient subgroups has emerged as a potentially promising direction to improve the efficiency of Intensive Care Units (ICUs). By identif…